The need for efficient data compression, or more generally encoding, techniques is ever-increasing. For example, video coding is a central technology in a variety of applications, including consumer electronics, e.g., digital video disk (DVD) players/recorders, digital still cameras, mobile phones, etc., the Internet, e.g., streaming video applications, distance learning, surveillance and/or security applications, etc. In addition, audio compression has been used in a wide range of applications, such as music playback in Moving Picture Experts Group (MPEG) standards based applications, e.g., MPEG-1 Audio Layer 3 (MP3) players, computers, digital television, satellite radio, cable radio, etc.
Central to lossy data video compression is quantization. Quantization is the process of approximating a continuous range of input values, or a very large set of discrete input values, by a set of integer valued quantization indices. A video frame is segmented into “macroblocks” that are sequentially encoded. Each macroblock (MB) of the macroblocks can be encoded in one of two coding modes: intra-mode and inter-mode. In intra-mode, original MB data, e.g., pixels of blocks of the MB, are transform-coded without prediction. On the other hand, in inter-mode decoding, a MB is predicted from a previously decoded frame via motion compensation. Quantization can be applied to transform coefficients of the intra/inter-mode prediction error, or residue. In many scenarios, video coding systems employ scalar quantization, which operates on scalar input data, e.g., each input data is treated separately in producing an output, e.g., a finite set of values approximating the continuous range of input values.
A scalar quantizer for an input X, e.g., video data, includes two functions: (1) Classification function I=C[X], which selects an integer-valued class identifier I, or quantization index, at an encoder, based on the input X, and (2) Reconstruction function Y=R[I], which produces a reconstruction value of X (denoted by Y) at a decoder, based on the quantization index I. Conventional encoding techniques utilize a dead-zone (or deadzone) plus uniform threshold classification quantizer/uniform-reconstructor quantizer (DZ+UTQ/URQ) to approximate a range of input values. Although such techniques adjust a deadzone size associated with the DZ+UTQ, e.g., via a deadzone parameter (z) and/or rounding offset (f) to improve coding scalar quantizer coding efficiency, such techniques incur increased rate-distortion.
The above-described deficiencies of today's image processing techniques and related encoding technologies are merely intended to provide an overview of some of the problems of conventional technology, and are not intended to be exhaustive. Other problems with the state of the art, and corresponding benefits of some of the various non-limiting embodiments described herein, may become further apparent upon review of the following detailed description.